面向众核GPU加速系统的网络编码并行化及优化

网络编码允许网络节点在数据存储转发的基础上参与数据处理，已成为提高网络吞吐量、均衡网络负载和提高网络带宽利用率的有效方法，但是网络编码的计算复杂性严重影响了系统性能。基于众核GPU加速的系统可以充分利用众核GPU强大的计算能力和有效利用GPU的存储层次结构来优化加速网络编码。基于CUDA架构提出了以片段并行的技术来加速网络编码和基于纹理Cache的并行解码方法。利用提出的方法实现了线性随机编码，同时结合体系结构对其进行优化。实验结果显示，基于众核GPU的网络编码并行化技术是行之有效的，系统性能提升显著。

Parallelizing network coding on manycore GPU-accelerated system with optimization

It is well known that network coding has emerged as a promising technique to improve network throughput, balance network loads as well as better utilization of the available bandwidth of networks, in which intermediate nodes are allowed to perform processing operations on the incoming packets other than forwarding packets. But, its potential for practical use has remained to be a challenge, due to its high computational complexity which also severely damages its performance. However, system accelerated by many-core GPU can advance network coding with powerful computing capacity and optimized memory hierarchy from GPU. A fragment-based parallel coding and texture-based parallel decoding are proposed on CUDA-enable GPU. Moreover, random linear coding is parallelizing using CUDA with optimization based on proposed techniques. Experimental results demonstrate a remarkable performance improvement, and prove that it is extraordinarily effective to parallelize network coding on many-core GPU-accelerated system.